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IPFS Datasets Python - Complete Features List

This document provides a comprehensive list of all features and capabilities in IPFS Datasets Python.

🎯 Core Features

Mathematical Theorem Proving

Convert natural language (especially legal text) to verified formal logic:

  • Supported Provers: Z3, CVC5, Lean 4, Coq
  • Logic Types: Propositional logic, first-order logic, deontic logic
  • Use Cases: Legal document verification, contract analysis, compliance checking
  • Integration: SymbolicAI framework for automated theorem generation

Example:

from ipfs_datasets_py.logic_integration import LogicProcessor

processor = LogicProcessor()
result = processor.convert_to_logic("Citizens must vote")
proof = result.generate_proof()  # Verified formal proof

GraphRAG Document Processing

AI-powered document processing with knowledge graph integration:

  • PDF Analysis: Extract text, tables, images, and structure
  • Entity Extraction: Named entities, relationships, concepts
  • Knowledge Graphs: Build semantic networks from documents
  • Cross-Document Reasoning: Connect information across multiple sources
  • LLM Optimization: Prepare documents for optimal LLM consumption
  • Production Quality: 4,400+ test functions ensuring reliability

Example:

from ipfs_datasets_py.pdf_processing import PDFProcessor
from ipfs_datasets_py.search.logic_integration import LogicEnhancedRAG

pdf = PDFProcessor()
rag = LogicEnhancedRAG()

content = pdf.process("document.pdf")
rag.ingest_document(content, "doc1")
results = rag.query("What are the key findings?")

Universal File Conversion

Convert any file type to text for AI processing:

  • 60+ Supported Formats: Documents, spreadsheets, presentations, archives, media
  • Backend Selection: Automatic selection of best converter for each file type
  • Async/Sync Support: Both async and synchronous conversion modes
  • Error Handling: Comprehensive error handling with graceful fallbacks
  • Result Monads: Type-safe results with success/error handling

Example:

from ipfs_datasets_py.processors.file_converter import FileConverter

converter = FileConverter()
result = await converter.convert('document.pdf')
print(result.text)  # Extracted text ready for processing

Universal Media Processing

Download and process media from 1000+ platforms:

  • Platforms: YouTube, Vimeo, TikTok, Instagram, Twitter, Facebook, and 1000+ more
  • yt-dlp Integration: Advanced video/audio downloading
  • FFmpeg Processing: Video/audio conversion, transcoding, streaming
  • Metadata Extraction: Title, description, tags, timestamps
  • Quality Selection: Choose video/audio quality
  • Playlist Support: Download entire playlists or channels

Example:

import asyncio
from ipfs_datasets_py.data_transformation.multimedia import YtDlpWrapper

async def main():
	dl = YtDlpWrapper()
	result = await dl.download_video("https://youtube.com/watch?v=...")
	print(result.get("title"))

asyncio.run(main())

Knowledge Graph Intelligence

Advanced knowledge graph operations and reasoning:

  • Entity Recognition: NER with multiple models
  • Relationship Extraction: Identify connections between entities
  • Graph Construction: Build semantic networks
  • Graph Traversal: Query and explore knowledge graphs
  • Semantic Search: Find related concepts and entities
  • Cross-Document Links: Connect information across sources

Decentralized Storage (IPFS)

Native IPFS integration for content-addressed storage:

  • ipfs_kit_py Integration: Comprehensive IPFS operations
  • Content Addressing: CID-based content identification
  • Pinning Management: Keep important content available
  • IPLD Support: InterPlanetary Linked Data
  • CAR Archives: Content Archive format support
  • Gateway Access: Public and private gateway support

Example:

from ipfs_datasets_py.ipfs_datasets import ipfs_datasets

ipfs = ipfs_datasets()
cid = ipfs.add_file("data.json")
content = ipfs.get_file(cid)
ipfs.pin(cid)  # Keep content available

Hardware Acceleration

Significant performance improvements with multi-backend acceleration:

  • ipfs_accelerate_py Integration: 2-20x speedup for operations
  • Multi-Backend Support: GPU, CUDA, CPU optimization
  • Automatic Selection: Choose best backend for each operation
  • Batch Processing: Efficient batch operations
  • Memory Optimization: Reduced memory usage

🤖 MCP Server (Model Context Protocol)

Tool Categories (200+ tools across 50+ categories)

Dataset Operations:

  • dataset_tools - Load, process, and manage datasets
  • embedding_tools - Generate and manage embeddings
  • vector_tools - Vector similarity search and operations

Web & Data Collection:

  • web_archive_tools - Web scraping, yt-dlp, Common Crawl
  • legal_dataset_tools - Legal document scraping (US Code, state laws, PACER)
  • media_tools - Multimedia download and processing

Document Processing:

  • pdf_tools - PDF processing and analysis
  • knowledge_graph_tools - Entity extraction and graph operations
  • file_conversion_tools - Universal file format conversion

IPFS & Storage:

  • ipfs_tools - IPFS operations (add, get, pin, cat)
  • storage_tools - Multi-backend storage management
  • cache_tools - Caching strategies and optimization

Infrastructure:

  • p2p_tools - Distributed computing and workflows
  • monitoring_tools - System monitoring and metrics
  • admin_tools - Administrative operations
  • security_tools - Security and audit features

Development:

  • software_engineering_tools - Auto-healing, PR review, code analysis
  • development_tools - Development utilities
  • vscode_cli_tools - VSCode CLI integration
  • github_cli_tools - GitHub CLI integration

MCP Dashboard

Real-time monitoring and analytics:

  • Investigation Tracking - GitHub issues → MCP tools → AI suggestions
  • Tool Usage Analytics - Execution times, success rates, usage patterns
  • System Monitoring - Resource usage, health checks, error tracking
  • WebSocket Updates - Live real-time updates

🔧 Production Features

Auto-Fix with GitHub Copilot

Automated code fixing and maintenance:

  • Workflow Failure Detection - Monitors 13+ GitHub Actions workflows
  • Automatic Issue Creation - Creates issues for failures
  • Draft PR Generation - Generates PRs with @copilot mentions
  • 100% Verification - All fixes verified before deployment

Automatic Error Reporting

Runtime error handling and reporting:

  • Exception Capture - Capture and analyze runtime errors
  • GitHub Issue Creation - Automatic issue creation
  • Stack Trace Analysis - Detailed error analysis
  • Error Categorization - Group similar errors

Production Monitoring

Comprehensive monitoring and observability:

  • Prometheus Metrics - Industry-standard metrics
  • Health Checks - Service health monitoring
  • Resource Tracking - CPU, memory, disk usage
  • Performance Monitoring - Latency, throughput, error rates
  • Unified Dashboard - Single pane of glass for all metrics

Distributed Computing

P2P networking and distributed workflows:

  • libp2p Integration - Peer-to-peer networking
  • Distributed Task Queue - Distribute work across nodes
  • Workflow Orchestration - Complex workflow management
  • Node Discovery - Automatic peer discovery
  • Load Balancing - Distribute work efficiently

Enterprise Features

Production-ready enterprise capabilities:

  • Security & Audit Logging - Comprehensive audit trails
  • Data Provenance - Track data lineage and transformations
  • Access Control - Fine-grained permissions
  • Rate Limiting - Protect services from overload
  • Multi-tenancy - Isolated environments for multiple users

🛠️ CLI Tools

Command Categories

  • Info Commands - System status, version, configuration
  • MCP Server - Start, stop, status, logs
  • Tool Management - List, run, discover 200+ tools
  • VSCode CLI - VSCode integration and automation
  • GitHub CLI - GitHub operations and automation
  • Discord Integration - Send messages, files, webhooks
  • Email Integration - Send emails, check status

📦 Core Modules

Data Processing

  1. dataset_manager - Dataset loading and management
  2. file_converter - Universal file format conversion
  3. embeddings - Vector embeddings and similarity

Document Intelligence

  1. pdf_processing - PDF analysis and extraction
  2. rag - Retrieval-Augmented Generation
  3. knowledge_graphs - Graph construction and querying

Logic & Proving

  1. logic_integration - Formal logic and theorem proving
  2. reasoning_coordinator - Multi-system reasoning

Storage & Infrastructure

  1. ipfs_datasets - Core IPFS operations
  2. ipfs_formats - IPFS format handling (CAR, IPLD)
  3. content_discovery - Content indexing and discovery

System

  1. config - Configuration management
  2. security - Authentication and authorization
  3. monitoring - Metrics and health checks

🔗 Integration Capabilities

External Integrations

  • HuggingFace Datasets - Direct integration
  • yt-dlp - 1000+ video platforms
  • FFmpeg - Video/audio processing
  • Common Crawl - Web-scale data access
  • Internet Archive - Historical web content

AI/ML Frameworks

  • Transformers - Hugging Face models
  • Sentence Transformers - Embeddings
  • SymbolicAI - Symbolic reasoning
  • Z3, CVC5, Lean 4, Coq - Theorem provers

Vector Databases

  • FAISS - Facebook AI Similarity Search
  • Qdrant - Vector search engine
  • Elasticsearch - Full-text and vector search
  • Chroma - AI-native vector database

Communication

  • Discord - Webhook and bot integration
  • Email - SMTP integration
  • GitHub - API integration
  • Slack - (planned)

📊 Performance Characteristics

Benchmarks

  • Vector Search: <100ms for millions of vectors
  • PDF Processing: 10-50 pages/second
  • Embedding Generation: 100-1000 docs/sec (with GPU)
  • File Conversion: Varies by format and size
  • IPFS Operations: Network-dependent

Scalability

  • Dataset Size: Tested up to 100GB+
  • Concurrent Operations: 100+ simultaneous operations
  • Distributed: Multi-node deployment supported
  • Memory Efficiency: Memory-mapped file support

🔒 Security Features

Authentication & Authorization

  • API key management
  • OAuth integration (planned)
  • Role-based access control
  • Fine-grained permissions

Audit & Compliance

  • Comprehensive audit logging
  • Data provenance tracking
  • Compliance reporting
  • Security event monitoring

Data Security

  • Encryption at rest
  • Encryption in transit (TLS)
  • Secure secret management
  • Input validation and sanitization

🚀 Deployment Options

Supported Platforms

  • Docker - Single container deployment
  • Docker Compose - Multi-container orchestration
  • Kubernetes - Cloud-native deployment
  • Bare Metal - Direct server installation

Environments

  • Development - Local development setup
  • Staging - Pre-production testing
  • Production - Full production deployment
  • Edge - Edge computing nodes

📈 Future Roadmap

Planned Features

  • Multi-language support for theorem proving
  • Advanced AI agents for autonomous data processing
  • Blockchain integration for immutable audit trails
  • Enhanced visualization with interactive dashboards
  • Mobile SDKs for mobile app integration
  • Additional vector database backends
  • Expanded file format support
  • Real-time collaboration features

📚 Learn More

For the most up-to-date feature list, see the project documentation.